Custom stream processing

While the processing vocabulary of Akka Streams is quite rich (see the Streams Cookbook for examples) it
is sometimes necessary to define new transformation stages either because some functionality is missing from the
stock operations, or for performance reasons. In this part we show how to build custom processing stages and graph
junctions of various kinds.

Note

A custom graph stage should not be the first tool you reach for, defining graphs using flows
and the graph DSL is in general easier and does to a larger extent protect you from mistakes that
might be easy to make with a custom GraphStage

Custom processing with GraphStage

The GraphStage abstraction can be used to create arbitrary graph processing stages with any number of input
or output ports. It is a counterpart of the GraphDSL.create() method which creates new stream processing
stages by composing others. Where GraphStage differs is that it creates a stage that is itself not divisible into
smaller ones, and allows state to be maintained inside it in a safe way.

As a first motivating example, we will build a new Source that will simply emit numbers from 1 until it is
cancelled. To start, we need to define the "interface" of our stage, which is called shape in Akka Streams terminology
(this is explained in more detail in the section Modularity, Composition and Hierarchy). This is how this looks like:

importakka.stream.SourceShapeimportakka.stream.stage.GraphStageclassNumbersSourceextendsGraphStage[SourceShape[Int]]{// Define the (sole) output port of this stagevalout:Outlet[Int]=Outlet("NumbersSource")// Define the shape of this stage, which is SourceShape with the port we defined aboveoverridevalshape:SourceShape[Int]=SourceShape(out)// This is where the actual (possibly stateful) logic will liveoverridedefcreateLogic(inheritedAttributes:Attributes):GraphStageLogic=???}

As you see, in itself the GraphStage only defines the ports of this stage and a shape that contains the ports.
It also has, a currently unimplemented method called createLogic. If you recall, stages are reusable in multiple
materializations, each resulting in a different executing entity. In the case of GraphStage the actual running
logic is modeled as an instance of a GraphStageLogic which will be created by the materializer by calling
the createLogic method. In other words, all we need to do is to create a suitable logic that will emit the
numbers we want.

Note

It is very important to keep the GraphStage object itself immutable and reusable. All mutable state needs to be
confined to the GraphStageLogic that is created for every materialization.

In order to emit from a Source in a backpressured stream one needs first to have demand from downstream.
To receive the necessary events one needs to register a subclass of OutHandler with the output port
(Outlet). This handler will receive events related to the lifecycle of the port. In our case we need to
override onPull() which indicates that we are free to emit a single element. There is another callback,
onDownstreamFinish() which is called if the downstream cancelled. Since the default behavior of that callback is
to stop the stage, we don't need to override it. In the onPull callback we will simply emit the next number. This
is how it looks like in the end:

importakka.stream.SourceShapeimportakka.stream.Graphimportakka.stream.stage.GraphStageimportakka.stream.stage.OutHandlerclassNumbersSourceextendsGraphStage[SourceShape[Int]]{valout:Outlet[Int]=Outlet("NumbersSource")overridevalshape:SourceShape[Int]=SourceShape(out)overridedefcreateLogic(inheritedAttributes:Attributes):GraphStageLogic=newGraphStageLogic(shape){// All state MUST be inside the GraphStageLogic,// never inside the enclosing GraphStage.// This state is safe to access and modify from all the// callbacks that are provided by GraphStageLogic and the// registered handlers.privatevarcounter=1setHandler(out,newOutHandler{overridedefonPull():Unit={push(out,counter)counter+=1}})}}

Instances of the above GraphStage are subclasses of Graph[SourceShape[Int],NotUsed] which means
that they are already usable in many situations, but do not provide the DSL methods we usually have for other
Source s. In order to convert this Graph to a proper Source we need to wrap it using
Source.fromGraph (see Modularity, Composition and Hierarchy for more details about graphs and DSLs). Now we can use the
source as any other built-in one:

// A GraphStage is a proper Graph, just like what GraphDSL.create would returnvalsourceGraph:Graph[SourceShape[Int], NotUsed]=newNumbersSource// Create a Source from the Graph to access the DSLvalmySource:Source[Int, NotUsed]=Source.fromGraph(sourceGraph)// Returns 55valresult1:Future[Int]=mySource.take(10).runFold(0)(_+_)// The source is reusable. This returns 5050valresult2:Future[Int]=mySource.take(100).runFold(0)(_+_)

Similarly, to create a custom Sink one can register a subclass InHandler with the stage Inlet.
The onPush() callback is used to signal the handler a new element has been pushed to the stage,
and can hence be grabbed and used. onPush() can be overridden to provide custom behaviour.
Please note, most Sinks would need to request upstream elements as soon as they are created: this can be
done by calling pull(inlet) in the preStart() callback.

importakka.stream.SinkShapeimportakka.stream.stage.GraphStageimportakka.stream.stage.InHandlerclassStdoutSinkextendsGraphStage[SinkShape[Int]]{valin:Inlet[Int]=Inlet("StdoutSink")overridevalshape:SinkShape[Int]=SinkShape(in)overridedefcreateLogic(inheritedAttributes:Attributes):GraphStageLogic=newGraphStageLogic(shape){// This requests one element at the Sink startup.overridedefpreStart():Unit=pull(in)setHandler(in,newInHandler{overridedefonPush():Unit={println(grab(in))pull(in)}})}}

Port states, InHandler and OutHandler

In order to interact with a port (Inlet or Outlet) of the stage we need to be able to receive events
and generate new events belonging to the port. From the GraphStageLogic the following operations are available
on an output port:

push(out,elem) pushes an element to the output port. Only possible after the port has been pulled by downstream.

complete(out) closes the output port normally.

fail(out,exception) closes the port with a failure signal.

The events corresponding to an output port can be received in an OutHandler instance registered to the
output port using setHandler(out,handler). This handler has two callbacks:

onPull() is called when the output port is ready to emit the next element, push(out,elem) is now allowed
to be called on this port.

onDownstreamFinish() is called once the downstream has cancelled and no longer allows messages to be pushed to it.
No more onPull() will arrive after this event. If not overridden this will default to stopping the stage.

Also, there are two query methods available for output ports:

isAvailable(out) returns true if the port can be pushed

isClosed(out) returns true if the port is closed. At this point the port can not be pushed and will not be pulled anymore.

The relationship of the above operations, events and queries are summarized in the state machine below. Green shows
the initial state while orange indicates the end state. If an operation is not listed for a state, then it is invalid
to call it while the port is in that state. If an event is not listed for a state, then that event cannot happen
in that state.

The following operations are available for input ports:

pull(in) requests a new element from an input port. This is only possible after the port has been pushed by upstream.

grab(in) acquires the element that has been received during an onPush(). It cannot be called again until the
port is pushed again by the upstream.

cancel(in) closes the input port.

The events corresponding to an input port can be received in an InHandler instance registered to the
input port using setHandler(in,handler). This handler has three callbacks:

onPush() is called when the input port has now a new element. Now it is possible to acquire this element using
grab(in) and/or call pull(in) on the port to request the next element. It is not mandatory to grab the
element, but if it is pulled while the element has not been grabbed it will drop the buffered element.

onUpstreamFinish() is called once the upstream has completed and no longer can be pulled for new elements.
No more onPush() will arrive after this event. If not overridden this will default to stopping the stage.

onUpstreamFailure() is called if the upstream failed with an exception and no longer can be pulled for new elements.
No more onPush() will arrive after this event. If not overridden this will default to failing the stage.

Also, there are three query methods available for input ports:

isAvailable(in) returns true if the port can be grabbed.

hasBeenPulled(in) returns true if the port has been already pulled. Calling pull(in) in this state is illegal.

isClosed(in) returns true if the port is closed. At this point the port can not be pulled and will not be pushed anymore.

The relationship of the above operations, events and queries are summarized in the state machine below. Green shows
the initial state while orange indicates the end state. If an operation is not listed for a state, then it is invalid
to call it while the port is in that state. If an event is not listed for a state, then that event cannot happen
in that state.

Finally, there are two methods available for convenience to complete the stage and all of its ports:

completeStage() is equivalent to closing all output ports and cancelling all input ports.

failStage(exception) is equivalent to failing all output ports and cancelling all input ports.

In some cases it is inconvenient and error prone to react on the regular state machine events with the
signal based API described above. For those cases there is an API which allows for a more declarative sequencing
of actions which will greatly simplify some use cases at the cost of some extra allocations. The difference
between the two APIs could be described as that the first one is signal driven from the outside, while this API
is more active and drives its surroundings.

The operations of this part of the :class:GraphStage API are:

emit(out,elem) and emitMultiple(out,Iterable(elem1,elem2)) replaces the OutHandler with a handler that emits
one or more elements when there is demand, and then reinstalls the current handlers

read(in)(andThen) and readN(in,n)(andThen) replaces the InHandler with a handler that reads one or
more elements as they are pushed and allows the handler to react once the requested number of elements has been read.

abortEmitting() and abortReading() which will cancel an ongoing emit or read

Note that since the above methods are implemented by temporarily replacing the handlers of the stage you should never
call setHandler while they are running emit or read as that interferes with how they are implemented.
The following methods are safe to call after invoking emit and read (and will lead to actually running the
operation when those are done): complete(out), completeStage(), emit, emitMultiple, abortEmitting()
and abortReading()

An example of how this API simplifies a stage can be found below in the second version of the :class:Duplicator.

Custom linear processing stages using GraphStage

Graph stages allows for custom linear processing stages through letting them
have one input and one output and using FlowShape as their shape.

Such a stage can be illustrated as a box with two flows as it is
seen in the illustration below. Demand flowing upstream leading to elements
flowing downstream.

To illustrate these concepts we create a small GraphStage that implements the map transformation.

Map calls push(out) from the onPush() handler and it also calls pull() from the onPull handler resulting in the
conceptual wiring above, and fully expressed in code below:

Map is a typical example of a one-to-one transformation of a stream where
demand is passed along upstream elements passed on downstream.

To demonstrate a many-to-one stage we will implement
filter. The conceptual wiring of Filter looks like this:

As we see above, if the given predicate matches the current element we are propagating it downwards, otherwise
we return the “ball” to our upstream so that we get the new element. This is achieved by modifying the map
example by adding a conditional in the onPush handler and decide between a pull(in) or push(out) call
(and of course not having a mapping f function).

To complete the picture we define a one-to-many transformation as the next step. We chose a straightforward example stage
that emits every upstream element twice downstream. The conceptual wiring of this stage looks like this:

This is a stage that has state: an option with the last element it has seen indicating if it
has duplicated this last element already or not. We must also make sure to emit the extra element
if the upstream completes.

classDuplicator[A]extendsGraphStage[FlowShape[A, A]]{valin=Inlet[A]("Duplicator.in")valout=Outlet[A]("Duplicator.out")valshape=FlowShape.of(in,out)overridedefcreateLogic(inheritedAttributes:Attributes):GraphStageLogic=newGraphStageLogic(shape){// Again: note that all mutable state// MUST be inside the GraphStageLogicvarlastElem:Option[A]=NonesetHandler(in,newInHandler{overridedefonPush():Unit={valelem=grab(in)lastElem=Some(elem)push(out,elem)}overridedefonUpstreamFinish():Unit={if(lastElem.isDefined)emit(out,lastElem.get)complete(out)}})setHandler(out,newOutHandler{overridedefonPull():Unit={if(lastElem.isDefined){push(out,lastElem.get)lastElem=None}else{pull(in)}}})}}

In this case a pull from downstream might be consumed by the stage itself rather
than passed along upstream as the stage might contain an element it wants to
push. Note that we also need to handle the case where the upstream closes while
the stage still has elements it wants to push downstream. This is done by
overriding onUpstreamFinish in the InHandler and provide custom logic
that should happen when the upstream has been finished.

This example can be simplified by replacing the usage of a mutable state with calls to
emitMultiple which will replace the handlers, emit each of multiple elements and then
reinstate the original handlers:

classDuplicator[A]extendsGraphStage[FlowShape[A, A]]{valin=Inlet[A]("Duplicator.in")valout=Outlet[A]("Duplicator.out")valshape=FlowShape.of(in,out)overridedefcreateLogic(inheritedAttributes:Attributes):GraphStageLogic=newGraphStageLogic(shape){setHandler(in,newInHandler{overridedefonPush():Unit={valelem=grab(in)// this will temporarily suspend this handler until the two elems// are emitted and then reinstates itemitMultiple(out,Iterable(elem,elem))}})setHandler(out,newOutHandler{overridedefonPull():Unit={pull(in)}})}}

Finally, to demonstrate all of the stages above, we put them together into a processing chain,
which conceptually would correspond to the following structure:

In code this is only a few lines, using the via use our custom stages in a stream:

If we attempt to draw the sequence of events, it shows that there is one "event token"
in circulation in a potential chain of stages, just like our conceptual "railroad tracks" representation predicts.

Completion

Completion handling usually (but not exclusively) comes into the picture when processing stages need to emit
a few more elements after their upstream source has been completed. We have seen an example of this in our
first Duplicator implementation where the last element needs to be doubled even after the upstream neighbor
stage has been completed. This can be done by overriding the onUpstreamFinish method in InHandler.

Stages by default automatically stop once all of their ports (input and output) have been closed externally or internally.
It is possible to opt out from this behavior by invoking setKeepGoing(true) (which is not supported from the stage’s
constructor and usually done in preStart). In this case the stage must be explicitly closed by calling completeStage()
or failStage(exception). This feature carries the risk of leaking streams and actors, therefore it should be used
with care.

Logging inside GraphStages

Logging debug or other important information in your stages is often a very good idea, especially when developing
more advances stages which may need to be debugged at some point.

The helper trait akka.stream.stage.StageLogging is provided to enable you to easily obtain a LoggingAdapter
inside of a GraphStage as long as the Materializer you're using is able to provide you with a logger.
In that sense, it serves a very similar purpose as ActorLogging does for Actors.

Note

Please note that you can always simply use a logging library directly inside a Stage.
Make sure to use an asynchronous appender however, to not accidentally block the stage when writing to files etc.
See Using the SLF4J API directly for more details on setting up async appenders in SLF4J.

The stage then gets access to the log field which it can safely use from any GraphStage callbacks:

SPI Note: If you're implementing a Materializer, you can add this ability to your materializer by implementing
MaterializerLoggingProvider in your Materializer.

Using timers

It is possible to use timers in GraphStages by using TimerGraphStageLogic as the base class for
the returned logic. Timers can be scheduled by calling one of scheduleOnce(key,delay), schedulePeriodically(key,period) or
schedulePeriodicallyWithInitialDelay(key,delay,period) and passing an object as a key for that timer (can be any object, for example
a String). The onTimer(key) method needs to be overridden and it will be called once the timer of key
fires. It is possible to cancel a timer using cancelTimer(key) and check the status of a timer with
isTimerActive(key). Timers will be automatically cleaned up when the stage completes.

Timers can not be scheduled from the constructor of the logic, but it is possible to schedule them from the
preStart() lifecycle hook.

In this sample the stage toggles between open and closed, where open means no elements are passed through. The
stage starts out as closed but as soon as an element is pushed downstream the gate becomes open for a duration
of time during which it will consume and drop upstream messages:

// each time an event is pushed through it will trigger a period of silenceclassTimedGate[A](silencePeriod:FiniteDuration)extendsGraphStage[FlowShape[A, A]]{valin=Inlet[A]("TimedGate.in")valout=Outlet[A]("TimedGate.out")valshape=FlowShape.of(in,out)overridedefcreateLogic(inheritedAttributes:Attributes):GraphStageLogic=newTimerGraphStageLogic(shape){varopen=falsesetHandler(in,newInHandler{overridedefonPush():Unit={valelem=grab(in)if(open)pull(in)else{push(out,elem)open=truescheduleOnce(None,silencePeriod)}}})setHandler(out,newOutHandler{overridedefonPull():Unit={pull(in)}})overrideprotecteddefonTimer(timerKey:Any):Unit={open=false}}}

Using asynchronous side-channels

In order to receive asynchronous events that are not arriving as stream elements (for example a completion of a future
or a callback from a 3rd party API) one must acquire a AsyncCallback by calling getAsyncCallback() from the
stage logic. The method getAsyncCallback takes as a parameter a callback that will be called once the asynchronous
event fires. It is important to not call the callback directly, instead, the external API must call the
invoke(event) method on the returned AsyncCallback. The execution engine will take care of calling the
provided callback in a thread-safe way. The callback can safely access the state of the GraphStageLogic
implementation.

Sharing the AsyncCallback from the constructor risks race conditions, therefore it is recommended to use the
preStart() lifecycle hook instead.

This example shows an asynchronous side channel graph stage that starts dropping elements
when a future completes:

// will close upstream in all materializations of the graph stage instance// when the future completesclassKillSwitch[A](switch:Future[Unit])extendsGraphStage[FlowShape[A, A]]{valin=Inlet[A]("KillSwitch.in")valout=Outlet[A]("KillSwitch.out")valshape=FlowShape.of(in,out)overridedefcreateLogic(inheritedAttributes:Attributes):GraphStageLogic=newGraphStageLogic(shape){overridedefpreStart():Unit={valcallback=getAsyncCallback[Unit]{(_)=>completeStage()}switch.foreach(callback.invoke)}setHandler(in,newInHandler{overridedefonPush():Unit={push(out,grab(in))}})setHandler(out,newOutHandler{overridedefonPull():Unit={pull(in)}})}}

Integration with actors

This section is a stub and will be extended in the next releaseThis is an experimental feature*

It is possible to acquire an ActorRef that can be addressed from the outside of the stage, similarly how
AsyncCallback allows injecting asynchronous events into a stage logic. This reference can be obtained
by calling getStageActorRef(receive) passing in a function that takes a Pair of the sender
ActorRef and the received message. This reference can be used to watch other actors by calling its watch(ref)
or unwatch(ref) methods. The reference can be also watched by external actors. The current limitations of this
ActorRef are:

they are not location transparent, they cannot be accessed via remoting.

they cannot be returned as materialized values.

they cannot be accessed from the constructor of the GraphStageLogic, but they can be accessed from the
preStart() method.

Custom materialized values

Custom stages can return materialized values instead of NotUsed by inheriting from GraphStageWithMaterializedValue
instead of the simpler GraphStage. The difference is that in this case the method
createLogicAndMaterializedValue(inheritedAttributes) needs to be overridden, and in addition to the
stage logic the materialized value must be provided

Warning

There is no built-in synchronization of accessing this value from both of the thread where the logic runs and
the thread that got hold of the materialized value. It is the responsibility of the programmer to add the
necessary (non-blocking) synchronization and visibility guarantees to this shared object.

In this sample the materialized value is a future containing the first element to go through the stream:

classFirstValue[A]extendsGraphStageWithMaterializedValue[FlowShape[A, A], Future[A]]{valin=Inlet[A]("FirstValue.in")valout=Outlet[A]("FirstValue.out")valshape=FlowShape.of(in,out)overridedefcreateLogicAndMaterializedValue(inheritedAttributes:Attributes):(GraphStageLogic,Future[A])={valpromise=Promise[A]()vallogic=newGraphStageLogic(shape){setHandler(in,newInHandler{overridedefonPush():Unit={valelem=grab(in)promise.success(elem)push(out,elem)// replace handler with one just forwardingsetHandler(in,newInHandler{overridedefonPush():Unit={push(out,grab(in))}})}})setHandler(out,newOutHandler{overridedefonPull():Unit={pull(in)}})}(logic,promise.future)}}

Using attributes to affect the behavior of a stage

This section is a stub and will be extended in the next release

Stages can access the Attributes object created by the materializer. This contains all the applied (inherited)
attributes applying to the stage, ordered from least specific (outermost) towards the most specific (innermost)
attribute. It is the responsibility of the stage to decide how to reconcile this inheritance chain to a final effective
decision.

Rate decoupled graph stages

Sometimes it is desirable to decouple the rate of the upstream and downstream of a stage, synchronizing only
when needed.

This is achieved in the model by representing a GraphStage as a boundary between two regions where the
demand sent upstream is decoupled from the demand that arrives from downstream. One immediate consequence of this
difference is that an onPush call does not always lead to calling push and an onPull call does not always
lead to calling pull.

One of the important use-case for this is to build buffer-like entities, that allow independent progress
of upstream and downstream stages when the buffer is not full or empty, and slowing down the appropriate side if the
buffer becomes empty or full.

The next diagram illustrates the event sequence for a buffer with capacity of two elements in a setting where
the downstream demand is slow to start and the buffer will fill up with upstream elements before any demand
is seen from downstream.

Another scenario would be where the demand from downstream starts coming in before any element is pushed
into the buffer stage.

The first difference we can notice is that our Buffer stage is automatically pulling its upstream on
initialization. The buffer has demand for up to two elements without any downstream demand.

The following code example demonstrates a buffer class corresponding to the message sequence chart above.

classTwoBuffer[A]extendsGraphStage[FlowShape[A, A]]{valin=Inlet[A]("TwoBuffer.in")valout=Outlet[A]("TwoBuffer.out")valshape=FlowShape.of(in,out)overridedefcreateLogic(inheritedAttributes:Attributes):GraphStageLogic=newGraphStageLogic(shape){valbuffer=mutable.Queue[A]()defbufferFull=buffer.size==2vardownstreamWaiting=falseoverridedefpreStart():Unit={// a detached stage needs to start upstream demand// itself as it is not triggered by downstream demandpull(in)}setHandler(in,newInHandler{overridedefonPush():Unit={valelem=grab(in)buffer.enqueue(elem)if(downstreamWaiting){downstreamWaiting=falsevalbufferedElem=buffer.dequeue()push(out,bufferedElem)}if(!bufferFull){pull(in)}}overridedefonUpstreamFinish():Unit={if(buffer.nonEmpty){// emit the rest if possibleemitMultiple(out,buffer.toIterator)}completeStage()}})setHandler(out,newOutHandler{overridedefonPull():Unit={if(buffer.isEmpty){downstreamWaiting=true}else{valelem=buffer.dequeuepush(out,elem)}if(!bufferFull&&!hasBeenPulled(in)){pull(in)}}})}}

Thread safety of custom processing stages

All of the above custom stages (linear or graph) provide a few simple guarantees that implementors can rely on.

The callbacks exposed by all of these classes are never called concurrently.

The state encapsulated by these classes can be safely modified from the provided callbacks, without any further
synchronization.

In essence, the above guarantees are similar to what Actor s provide, if one thinks of the state of a custom
stage as state of an actor, and the callbacks as the receive block of the actor.

Warning

It is not safe to access the state of any custom stage outside of the callbacks that it provides, just like it
is unsafe to access the state of an actor from the outside. This means that Future callbacks should not close over
internal state of custom stages because such access can be concurrent with the provided callbacks, leading to undefined
behavior.

Resources and the stage lifecycle

If a stage manages a resource with a lifecycle, for example objects that need to be shutdown when they are not
used anymore it is important to make sure this will happen in all circumstances when the stage shuts down.

Cleaning up resources should be done in GraphStageLogic.postStop and not in the InHandler and OutHandler
callbacks. The reason for this is that when the stage itself completes or is failed there is no signal from the upstreams
or the downstreams. Even for stages that do not complete or fail in this manner, this can happen when the
Materializer is shutdown or the ActorSystem is terminated while a stream is still running, what is called an
"abrupt termination".

Extending Flow Combinators with Custom Operators

The most general way of extending any Source, Flow or SubFlow (e.g. from groupBy) is
demonstrated above: create a graph of flow-shape like the Duplicator example given above and use the .via(...)
combinator to integrate it into your stream topology. This works with all FlowOps sub-types, including the
ports that you connect with the graph DSL.

Advanced Scala users may wonder whether it is possible to write extension methods that enrich FlowOps to
allow nicer syntax. The short answer is that Scala 2 does not support this in a fully generic fashion, the problem is
that it is impossible to abstract over the kind of stream that is being extended because Source, Flow
and SubFlow differ in the number and kind of their type parameters. While it would be possible to write
an implicit class that enriches them generically, this class would require explicit instantiation with all type
parameters due to SI-2712. For a partial workaround that unifies
extensions to Source and Flow see this sketch by R. Kuhn.

A lot simpler is the task of just adding an extension method to Source as shown below:

If you try to write this for SubFlow, though, you will run into the same issue as when trying to unify
the two solutions above, only on a higher level (the type constructors needed for that unification would have rank
two, meaning that some of their type arguments are type constructors themselves—when trying to extend the solution
shown in the linked sketch the author encountered such a density of compiler StackOverflowErrors and IDE failures
that he gave up).

It is interesting to note that a simplified form of this problem has found its way into the dotty test suite.
Dotty is the development version of Scala on its way to Scala 3.